Comprehensive profiling of the human fecal proteome from IBD patients with DIA-MS enables evaluation of disease-relevant proteins.

PURPOSE: Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn's disease (CD), is characterized by chronic gastrointestinal inflammation. A high unmet need exists for noninvasive biomarkers in IBD to monitor changes in disease activity and guide treatment decisions. Stool is an easily accessed, disease proximal matrix in IBD, however the composition of the IBD fecal proteome remains poorly characterized. EXPERIMENTAL DESIGN: A data-independent acquisition LC-MS/MS approach was used to profile the human fecal proteome in two independent cohorts (Cohort 1: healthy n = 5, UC n = 5, CD n = 5, Cohort 2: healthy n = 20, UC n = 10, and CD n = 10) to identify noninvasive biomarkers reflective of disease activity. RESULTS: 688 human proteins were quantified, with 523 measured in both cohorts. In UC stool 96 proteins were differentially abundant and in CD stool 126 proteins were differentially abundant compared to healthy stool (absolute log2 fold change > 1, p-value < 0.05). Many of these fecal proteins are associated with infiltrating immune cells and ulceration/rectal bleeding, which are hallmarks of IBD pathobiology. Mapping the identified fecal proteins to a whole blood single-cell RNA sequencing data set revealed the involvement of various immune cell subsets to the IBD fecal proteome. CONCLUSIONS AND CLINICAL RELEVANCE: Findings from this study not only confirmed the presence of established fecal biomarkers for IBD, such as calprotectin and lactoferrin, but also revealed new fecal proteins from multiple pathways known to be dysregulated in IBD. These novel proteins could serve as potential noninvasive biomarkers to monitor specific aspects of IBD disease activity which could expedite clinical development of novel therapeutic targets.

Using SILAC to Develop Quantitative Data-Independent Acquisition (DIA) Proteomic Methods.

Proteins are integral to biological systems and functions. Identifying and quantifying proteins can therefore offer systems-wide insights into protein-protein interactions, cellular signaling, and biological pathway activity. The use of quantitative proteomics has become a method of choice for identifying and quantifying proteins in complex matrices. Proteomics allows researchers to survey hundreds to thousands of proteins in a less biased manner than classical antibody-based protein capture strategies. Typically, discovery approaches have used data-dependent acquisition (DDA) methods, but this approach suffers from stochasticity that compromises quantitation. Recent developments in data-independent acquisition (DIA) proteomics workflows enable proteomic profiling of thousands of samples with increased peak picking consistency making it an excellent candidate for discovering and assessing biomarkers in clinical samples. However, quantitation of peptides from DIA datasets is computationally intensive, and guidelines on how to establish DIA methods are lacking. Method development and optimization require novel tools to visualize and filter DIA datasets appropriately. Here, a protocol and novel script workflow for the optimization of quantitative DIA methods using stable isotope labeling of amino acids in culture (SILAC) are presented. This protocol includes steps for cell growth and labeling, peptide digestion and preparation, and optimization of quantitative DIA methods. In addition, important steps for (1) computational analysis to identify and quantify peptides, (2) data visualizations to identify the linear abundance ranges for all peptides in the sample, and (3) descriptions of how to find high confidence quantitation abundance thresholds are described herein.

Latent-period stool proteomic assay of multiple sclerosis model indicates protective capacity of host-expressed protease inhibitors.

Diseases are often diagnosed once overt symptoms arise, ignoring the prior latent period when effective prevention may be possible. Experimental autoimmune encephalomyelitis (EAE), a model for multiple sclerosis, exhibits such disease latency, but the molecular processes underlying this asymptomatic period remain poorly characterized. Gut microbes also influence EAE severity, yet their impact on the latent period remains unknown. Here, we show the latent period between immunization and EAE's overt symptom onset is characterized by distinct host responses as measured by stool proteomics. In particular, we found a transient increase in protease inhibitors that inversely correlated with disease severity. Vancomycin administration attenuated both EAE symptoms and protease inhibitor induction potentially by decreasing immune system reactivity, supporting a subset of the microbiota's role in modulating the host's latent period response. These results strengthen previous evidence of proteases and their inhibitors in EAE and highlight the utility stool-omics for revealing complex, dynamic biology.

Structure, biochemistry, and inhibition of essential 4'-phosphopantetheinyl transferases from two species of Mycobacteria.

4'-Phosphopantetheinyl transferases (PPTase) post-translationally modify carrier proteins with a phosphopantetheine moiety, an essential reaction in all three domains of life. In the bacterial genus Mycobacteria, the Sfp-type PPTase activates pathways necessary for the biosynthesis of cell wall components and small molecule virulence factors. We solved the X-ray crystal structures and biochemically characterized the Sfp-type PPTases from two of the most prevalent Mycobacterial pathogens, PptT of M. tuberculosis and MuPPT of M. ulcerans. Structural analyses reveal significant differences in cofactor binding and active site composition when compared to previously characterized Sfp-type PPTases. Functional analyses including the efficacy of Sfp-type PPTase-specific inhibitors also suggest that the Mycobacterial Sfp-type PPTases can serve as therapeutic targets against Mycobacterial infections.